{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-effective-single-image-super-resolution","title":"An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks","arxiv_id":"1810.01831","date":"2018-10-03","proceeding":null,"authors":["Kangfu Mei","Aiwen Jiang","Juncheng Li","Jihua Ye","Mingwen Wang"],"abstract":"Recent works on single-image super-resolution are concentrated on improving\nperformance through enhancing spatial encoding between convolutional layers. In\nthis paper, we focus on modeling the correlations between channels of\nconvolutional features. We present an effective deep residual network based on\nsqueeze-and-excitation blocks (SEBlock) to reconstruct high-resolution (HR)\nimage from low-resolution (LR) image. SEBlock is used to adaptively recalibrate\nchannel-wise feature mappings. Further, short connections between each SEBlock\nare used to remedy information loss. Extensive experiments show that our model\ncan achieve the state-of-the-art performance and get finer texture details.","url_abs":"http://arxiv.org/abs/1810.01831v1","url_pdf":"http://arxiv.org/pdf/1810.01831v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-effective-single-image-super-resolution","repo_url":"https://github.com/MKFMIKU/SrSENet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}